Deteksi Peluang Gagal Bayar Calon Debitur Menggunakan Algoritma Particle Swarm Optimization (PSO) untuk Meningkatkan Kinerja Manajemen Risiko pada Koperasi Simpan Pinjam ABC - Susan Purnama; Aninditha Putri Kusumawardhani (Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung, 2021)
Savings and Loan Cooperatives (KSP) are financial institutions that have an important role in economic and trade activities, useful for channeling funds in the form of loans to members who need them for business or business. In this paper, we examine the detection of potential debtors' default opportunities using the Particle Swarm Optimization (PSO) algorithm in a logistic regression model. In the analysis method, there are several steps: (1) standardizing the data on the risk factor data of prospective debtors, (2) determining the assumptions of the logistic regression model, (3) estimating the parameters of the logistic regression model using the Particle Swarm Optimization (PSO) algorithm, and (4 ) to test the significance of each variable. The probability of default is determined using the eligibility parameters of the prospective debtor based on past data variables owned by KSP